发表机构
University of New South Wales; MBZUAI（新南威尔士大学; 穆罕默德·本·扎耶德人工智能大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出BARRAC，将英文基于方面的情感分析框架适配至阿拉伯语分类任务，在五个方言数据集上平均宏F1达63.93%，优于现有少标签方法及GPT-4o，证明任务特定方法适配的有效性。
AI 中文摘要
随着阿拉伯语自然语言处理的快速发展，已有多个模型、数据集和基准被报道。本文探讨了为多数语言（如英语）开发的方法能否适配到阿拉伯语任务中。我们将一个英文基于方面的情感分析框架适配到阿拉伯语分类任务，并将该适配方法命名为BARRAC：面向阿拉伯语任务的头脑风暴对齐与替换表示学习。BARRAC将消费者评论属性池替换为阿拉伯语语言设备和标记，用于方言情感、讽刺和方言识别，并将有噪声的自训练替换为两阶段训练。在五个阿拉伯方言数据集上的评估中，BARRAC实现了平均宏F1分数63.93%，优于最佳少标签最先进方法3%，并在五个任务中的四个上优于GPT-4o。错误分析提供了对剩余挑战的见解。这些结果表明，适配任务特定方法是阿拉伯语自然语言处理中除适配模型、数据集和基准之外的一个有前景的方向。
英文摘要
With the rapid growth of Arabic NLP, several models, datasets and benchmarks have been reported. This paper asks whether approaches developed for majority languages like English can be adapted to Arabic tasks. We adapt an English aspect-based sentiment analysis framework to Arabic classification tasks and present the adaptation as BARRAC: Brainstorming Alignment and Replaced Representation learning for ArabiC tasks. BARRAC replaces consumer-review attribute pools with Arabic linguistic devices and markers for dialectal sentiment, sarcasm, and dialect identification, and replaces noisy self-training with two-stage training. Evaluated on five Arabic dialect datasets, BARRAC achieves a mean macro-F1 of 63.93\%, outperforming the best few-label SOTA by 3\%, and outperforming GPT-4o on four out of five tasks. Error analysis provides insights into remaining challenges. These results demonstrate that adapting task-specific approaches is a promising direction for Arabic NLP alongside adapting models, datasets and benchmarks.
